@inproceedings{paiement:2006:cai,
  author = {J.-F. Paiement and D. Eck and S. Bengio},
  title = {Probabilistic Melodic Harmonization},
  booktitle = {Advances in Artificial Intelligence: 19th Conference of the Canadian Society for Computational Studies of Intelligence, {Canadian AI}, Lecture Notes in Computer Science},
  publisher = {Springer-Verlag},
  editor = {L. Lamontagne and M. Marchand},
  volume = {LNCS 4013},
  year = 2006,
  pages = {218--229},
  url = {publications/ps/paiement_2006_cai.ps.gz},
  pdf = {publications/pdf/paiement_2006_cai.pdf},
  djvu = {publications/djvu/paiement_2006_cai.djvu},
  original = {2006/melodic_can_ai},
  topics = {graphical_models},
  web = {http://dx.doi.org/10.1007/11766247_19},
  abstract = {We propose a representation for musical chords that allows us to include domain knowledge in probabilistic models. We then introduce a graphical model for harmonization of melodies that considers every structural components in chord notation. We show empirically that root notes progressions exhibit global dependencies that can be better captured with a tree structure related to the meter than with a simple dynamical HMM that concentrates on local dependencies.  However, a local model seems to be sufficient for generating proper harmonizations when root notes progressions are provided.  The trained probabilistic models can be sampled to generate very interesting chord progressions given other polyphonic music components such as melody or root note progressions.},
  categorie = {C},
}

{"_id":"2sQSSMvtMW42H5DMX","bibbaseid":"paiement-eck-bengio-probabilisticmelodicharmonization-2006","authorIDs":[],"author_short":["Paiement, J.","Eck, D.","Bengio, S."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["J.-F."],"propositions":[],"lastnames":["Paiement"],"suffixes":[]},{"firstnames":["D."],"propositions":[],"lastnames":["Eck"],"suffixes":[]},{"firstnames":["S."],"propositions":[],"lastnames":["Bengio"],"suffixes":[]}],"title":"Probabilistic Melodic Harmonization","booktitle":"Advances in Artificial Intelligence: 19th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI, Lecture Notes in Computer Science","publisher":"Springer-Verlag","editor":[{"firstnames":["L."],"propositions":[],"lastnames":["Lamontagne"],"suffixes":[]},{"firstnames":["M."],"propositions":[],"lastnames":["Marchand"],"suffixes":[]}],"volume":"LNCS 4013","year":"2006","pages":"218–229","url":"publications/ps/paiement_2006_cai.ps.gz","pdf":"publications/pdf/paiement_2006_cai.pdf","djvu":"publications/djvu/paiement_2006_cai.djvu","original":"2006/melodic_can_ai","topics":"graphical_models","web":"http://dx.doi.org/10.1007/11766247_19","abstract":"We propose a representation for musical chords that allows us to include domain knowledge in probabilistic models. We then introduce a graphical model for harmonization of melodies that considers every structural components in chord notation. We show empirically that root notes progressions exhibit global dependencies that can be better captured with a tree structure related to the meter than with a simple dynamical HMM that concentrates on local dependencies. However, a local model seems to be sufficient for generating proper harmonizations when root notes progressions are provided. The trained probabilistic models can be sampled to generate very interesting chord progressions given other polyphonic music components such as melody or root note progressions.","categorie":"C","bibtex":"@inproceedings{paiement:2006:cai,\n author = {J.-F. Paiement and D. Eck and S. Bengio},\n title = {Probabilistic Melodic Harmonization},\n booktitle = {Advances in Artificial Intelligence: 19th Conference of the Canadian Society for Computational Studies of Intelligence, {Canadian AI}, Lecture Notes in Computer Science},\n publisher = {Springer-Verlag},\n editor = {L. Lamontagne and M. Marchand},\n volume = {LNCS 4013},\n year = 2006,\n pages = {218--229},\n url = {publications/ps/paiement_2006_cai.ps.gz},\n pdf = {publications/pdf/paiement_2006_cai.pdf},\n djvu = {publications/djvu/paiement_2006_cai.djvu},\n original = {2006/melodic_can_ai},\n topics = {graphical_models},\n web = {http://dx.doi.org/10.1007/11766247_19},\n abstract = {We propose a representation for musical chords that allows us to include domain knowledge in probabilistic models. We then introduce a graphical model for harmonization of melodies that considers every structural components in chord notation. We show empirically that root notes progressions exhibit global dependencies that can be better captured with a tree structure related to the meter than with a simple dynamical HMM that concentrates on local dependencies. However, a local model seems to be sufficient for generating proper harmonizations when root notes progressions are provided. The trained probabilistic models can be sampled to generate very interesting chord progressions given other polyphonic music components such as melody or root note progressions.},\n categorie = {C},\n}\n\n","author_short":["Paiement, J.","Eck, D.","Bengio, S."],"editor_short":["Lamontagne, L.","Marchand, M."],"key":"paiement:2006:cai","id":"paiement:2006:cai","bibbaseid":"paiement-eck-bengio-probabilisticmelodicharmonization-2006","role":"author","urls":{"Paper":"http://bengio.abracadoudou.com/publications/ps/paiement_2006_cai.ps.gz"},"downloads":0},"bibtype":"inproceedings","biburl":"http://bengio.abracadoudou.com/samy.bib","creationDate":"2020-03-18T03:43:27.368Z","downloads":0,"keywords":[],"search_terms":["probabilistic","melodic","harmonization","paiement","eck","bengio"],"title":"Probabilistic Melodic Harmonization","year":2006,"dataSources":["9NCW2CDr4M3s5DvNX"]}